Priority-Based Optimized Scheduling (PBOS) Algorithm for Big Data Applications in Cloud Computing Environments

This paper proposes a priority-based optimized scheduling (PBOS) algorithm for cloud-based big data applications. In this algorithm, for each incoming task request, the task size and expected completion time are estimated. At the same time, for each VM on a host, the processing capability and current load are estimated. A priority queue for each task is then created in which the tasks are stored in the ascending order of task size and completion time. Similarly, a priority queue for VMs is also made in which the VMs are stored in the ascending order of current load and processing capacity. Then, the scheduling algorithm intelligently maps each user tasks from task queue to the suitable matching VM from VM queue. The process is continued until both these queue become empty. By experimental results, it is shown that PBOS achieves lesser response time and execution time.